{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "efebfd54-43d5-4621-b848-df73c5ae5f4f",
   "metadata": {},
   "source": [
    "# Baseline Models\n",
    "\n",
    "In this notebook, we will construct a series of models based on features that were extracted from 12-lead ECG data (i.e., not use the actual waveforms). \n",
    "\n",
    "These models were serve as a baseline for additional models to build off of and improve from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5667553d-7465-430c-828f-5112d14388cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install llvmlite --ignore-installed\n",
    "!pip install 'pycaret'\n",
    "!pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddc59752-1599-478c-9d16-4c436101e649",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install scipy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0029f188-d04f-4e75-9c00-c9ae7492fb7c",
   "metadata": {},
   "source": [
    "## Load extracted features\n",
    "\n",
    "Features have already been extracted in a separate script and saved to disk (long compute time).  \n",
    "\n",
    "For this analysis, we will just use CV, so we can combine the validation and training data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9049d2d9-5676-42a0-8d9e-621d3ead0dd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import pandas as pd\n",
    "\n",
    "def get_split(X_name, Y_name):\n",
    "    hf = h5py.File('data/model_data/feature_data.h5', 'r')\n",
    "    X = hf.get(X_name)\n",
    "    Y = hf.get(Y_name)\n",
    "    Y = pd.DataFrame(Y).to_numpy()\n",
    "    columns = hf.get(\"columns\")\n",
    "    columns = [col.decode('utf-8') for col in hf[\"columns\"][:]]\n",
    "    X = pd.DataFrame(data=X, columns = columns)\n",
    "    hf.close()\n",
    "    return X, Y\n",
    "\n",
    "X_train, Y_train = get_split(\"X_train\", \"Y_train\")\n",
    "X_val, Y_val = get_split(\"X_val\", \"Y_val\")\n",
    "X_test, Y_test = get_split(\"X_test\", \"Y_test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5de2b669-f8cd-4ed6-b86d-a8c784c18c46",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Combine train and validation sets\n",
    "X_train = pd.concat([X_train, X_val])\n",
    "Y_train = np.concatenate([Y_train, Y_val])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfebfd3a-afd5-4527-bdcf-4b480912ed57",
   "metadata": {},
   "source": [
    "## Initial Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "829ed45f-34fd-49ea-9c4a-40445bb5209e",
   "metadata": {},
   "source": [
    "Many columns that are primarily missing data or little/no variance.  Cut back on initial features here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b8354c18-9514-414c-a39b-c67cf3c80990",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/feature_selection/_variance_threshold.py:104: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
      "  self.variances_ = np.nanvar(X, axis=0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(17439, 64)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "\n",
    "# Before setting a variance threshold, will standardize variables to \n",
    "# put them all on a similar playing field\n",
    "normalized_df = X_train / X_train.mean()\n",
    "vt = VarianceThreshold(threshold=.003)\n",
    "_ = vt.fit(normalized_df)\n",
    "mask = vt.get_support()\n",
    "normalized_df_sel = normalized_df.loc[:, mask]\n",
    "normalized_df_sel = normalized_df_sel[normalized_df_sel.columns[normalized_df_sel.isnull().mean() < 0.4]]\n",
    "normalized_df_sel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7cfd72eb-1b9f-4d8f-885d-7b54c4c941eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "train = X_train[normalized_df_sel.columns]\n",
    "train = pd.DataFrame(train)\n",
    "\n",
    "train['target'] = Y_train\n",
    "train\n",
    "test = X_test[normalized_df_sel.columns]\n",
    "test['target'] = Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "446cb715-2994-4fb1-9c2a-e00c7a496e81",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(test.shape[1]):\n",
    "    test.iloc[:, i] = test.iloc[:, i].replace([np.inf, -np.inf], np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37154ea1-1fbc-4871-8f56-de9ee7a70c90",
   "metadata": {},
   "source": [
    "# Downsampling\n",
    "\n",
    "For this analysis, we will perform downsampling on the majority class to help with both compute and the large class imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "952b26df-3691-432a-a303-cc5124c3a772",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17439, 65)\n",
      "0    13521\n",
      "1     3918\n",
      "Name: target, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(train['target'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6b41d65f-f9fa-47ce-bba1-b5e5356740cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6000\n",
      "3918\n",
      "9918\n",
      "(9918, 65)\n"
     ]
    }
   ],
   "source": [
    "# Get indices of majority class\n",
    "class_0_idx = train[train.target == 0].index\n",
    "\n",
    "# Sample from majority class\n",
    "n_samples = 6000\n",
    "sampled_class_0_idx = np.random.choice(class_0_idx, size=n_samples, replace=False)\n",
    "\n",
    "# Samples from minority class\n",
    "class_1_idx = train[train.target == 1].index\n",
    "\n",
    "# Merge the selected samples\n",
    "idx_sampled = np.concatenate([sampled_class_0_idx, class_1_idx])\n",
    "idx_sampled.sort()\n",
    "\n",
    "# Filter the training data\n",
    "train = train.iloc[idx_sampled]\n",
    "\n",
    "print(len(sampled_class_0_idx))\n",
    "print(len(class_1_idx))\n",
    "print(len(idx_sampled))\n",
    "print(train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e025e307-123c-4453-a72c-c5bc30484d50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_65db8_row9_col1 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_65db8_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_65db8_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_65db8_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
       "      <td id=\"T_65db8_row0_col1\" class=\"data row0 col1\" >1279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65db8_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_65db8_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_65db8_row1_col1\" class=\"data row1 col1\" >target</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65db8_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_65db8_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
       "      <td id=\"T_65db8_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65db8_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_65db8_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
       "      <td id=\"T_65db8_row3_col1\" class=\"data row3 col1\" >(15392, 65)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65db8_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_65db8_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
       "      <td id=\"T_65db8_row4_col1\" class=\"data row4 col1\" >(31174, 65)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65db8_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_65db8_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
       "      <td id=\"T_65db8_row5_col1\" class=\"data row5 col1\" >(22265, 65)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65db8_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_65db8_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
       "      <td id=\"T_65db8_row6_col1\" class=\"data row6 col1\" >(8909, 65)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65db8_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_65db8_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
       "      <td id=\"T_65db8_row7_col1\" class=\"data row7 col1\" >64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65db8_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_65db8_row8_col0\" class=\"data row8 col0\" >Rows with missing values</td>\n",
       "      <td id=\"T_65db8_row8_col1\" class=\"data row8 col1\" >28.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65db8_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_65db8_row9_col0\" class=\"data row9 col0\" >Preprocess</td>\n",
       "      <td id=\"T_65db8_row9_col1\" class=\"data row9 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65db8_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_65db8_row10_col0\" class=\"data row10 col0\" >Imputation type</td>\n",
       "      <td id=\"T_65db8_row10_col1\" class=\"data row10 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65db8_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_65db8_row11_col0\" class=\"data row11 col0\" >Numeric imputation</td>\n",
       "      <td id=\"T_65db8_row11_col1\" class=\"data row11 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65db8_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_65db8_row12_col0\" class=\"data row12 col0\" >Categorical imputation</td>\n",
       "      <td id=\"T_65db8_row12_col1\" class=\"data row12 col1\" >mode</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65db8_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_65db8_row13_col0\" class=\"data row13 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_65db8_row13_col1\" class=\"data row13 col1\" >StratifiedKFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65db8_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_65db8_row14_col0\" class=\"data row14 col0\" >Fold Number</td>\n",
       "      <td id=\"T_65db8_row14_col1\" class=\"data row14 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65db8_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_65db8_row15_col0\" class=\"data row15 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_65db8_row15_col1\" class=\"data row15 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65db8_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_65db8_row16_col0\" class=\"data row16 col0\" >Use GPU</td>\n",
       "      <td id=\"T_65db8_row16_col1\" class=\"data row16 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65db8_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_65db8_row17_col0\" class=\"data row17 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_65db8_row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65db8_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_65db8_row18_col0\" class=\"data row18 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_65db8_row18_col1\" class=\"data row18 col1\" >clf-default-name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_65db8_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_65db8_row19_col0\" class=\"data row19 col0\" >USI</td>\n",
       "      <td id=\"T_65db8_row19_col1\" class=\"data row19 col1\" >7085</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fbde33b58d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.classification import *\n",
    "\n",
    "exp_name = setup(\n",
    "    data = train,\n",
    "    target = 'target',\n",
    "    fold_strategy='stratifiedkfold',\n",
    "    test_data=test\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d900fefb-7177-4fd6-bfae-ec9dc6e31733",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "output_type": "display_data"
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       "#T_efcf8_row0_col8, #T_efcf8_row1_col8, #T_efcf8_row2_col8, #T_efcf8_row3_col8, #T_efcf8_row4_col8, #T_efcf8_row5_col8, #T_efcf8_row6_col8, #T_efcf8_row7_col8, #T_efcf8_row9_col8, #T_efcf8_row10_col8, #T_efcf8_row11_col8, #T_efcf8_row12_col8, #T_efcf8_row13_col8 {\n",
       "  text-align: left;\n",
       "  background-color: lightgrey;\n",
       "}\n",
       "#T_efcf8_row8_col8 {\n",
       "  text-align: left;\n",
       "  background-color: yellow;\n",
       "  background-color: lightgrey;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_efcf8_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th class=\"col_heading level0 col1\" >Accuracy</th>\n",
       "      <th class=\"col_heading level0 col2\" >AUC</th>\n",
       "      <th class=\"col_heading level0 col3\" >Recall</th>\n",
       "      <th class=\"col_heading level0 col4\" >Prec.</th>\n",
       "      <th class=\"col_heading level0 col5\" >F1</th>\n",
       "      <th class=\"col_heading level0 col6\" >Kappa</th>\n",
       "      <th class=\"col_heading level0 col7\" >MCC</th>\n",
       "      <th class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_efcf8_level0_row0\" class=\"row_heading level0 row0\" >rf</th>\n",
       "      <td id=\"T_efcf8_row0_col0\" class=\"data row0 col0\" >Random Forest Classifier</td>\n",
       "      <td id=\"T_efcf8_row0_col1\" class=\"data row0 col1\" >0.7476</td>\n",
       "      <td id=\"T_efcf8_row0_col2\" class=\"data row0 col2\" >0.7584</td>\n",
       "      <td id=\"T_efcf8_row0_col3\" class=\"data row0 col3\" >0.4528</td>\n",
       "      <td id=\"T_efcf8_row0_col4\" class=\"data row0 col4\" >0.7384</td>\n",
       "      <td id=\"T_efcf8_row0_col5\" class=\"data row0 col5\" >0.5595</td>\n",
       "      <td id=\"T_efcf8_row0_col6\" class=\"data row0 col6\" >0.3969</td>\n",
       "      <td id=\"T_efcf8_row0_col7\" class=\"data row0 col7\" >0.4212</td>\n",
       "      <td id=\"T_efcf8_row0_col8\" class=\"data row0 col8\" >1.7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_efcf8_level0_row1\" class=\"row_heading level0 row1\" >gbc</th>\n",
       "      <td id=\"T_efcf8_row1_col0\" class=\"data row1 col0\" >Gradient Boosting Classifier</td>\n",
       "      <td id=\"T_efcf8_row1_col1\" class=\"data row1 col1\" >0.7416</td>\n",
       "      <td id=\"T_efcf8_row1_col2\" class=\"data row1 col2\" >0.7558</td>\n",
       "      <td id=\"T_efcf8_row1_col3\" class=\"data row1 col3\" >0.4599</td>\n",
       "      <td id=\"T_efcf8_row1_col4\" class=\"data row1 col4\" >0.7142</td>\n",
       "      <td id=\"T_efcf8_row1_col5\" class=\"data row1 col5\" >0.5577</td>\n",
       "      <td id=\"T_efcf8_row1_col6\" class=\"data row1 col6\" >0.3873</td>\n",
       "      <td id=\"T_efcf8_row1_col7\" class=\"data row1 col7\" >0.4071</td>\n",
       "      <td id=\"T_efcf8_row1_col8\" class=\"data row1 col8\" >7.1620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_efcf8_level0_row2\" class=\"row_heading level0 row2\" >lightgbm</th>\n",
       "      <td id=\"T_efcf8_row2_col0\" class=\"data row2 col0\" >Light Gradient Boosting Machine</td>\n",
       "      <td id=\"T_efcf8_row2_col1\" class=\"data row2 col1\" >0.7320</td>\n",
       "      <td id=\"T_efcf8_row2_col2\" class=\"data row2 col2\" >0.7535</td>\n",
       "      <td id=\"T_efcf8_row2_col3\" class=\"data row2 col3\" >0.4704</td>\n",
       "      <td id=\"T_efcf8_row2_col4\" class=\"data row2 col4\" >0.6792</td>\n",
       "      <td id=\"T_efcf8_row2_col5\" class=\"data row2 col5\" >0.5543</td>\n",
       "      <td id=\"T_efcf8_row2_col6\" class=\"data row2 col6\" >0.3719</td>\n",
       "      <td id=\"T_efcf8_row2_col7\" class=\"data row2 col7\" >0.3857</td>\n",
       "      <td id=\"T_efcf8_row2_col8\" class=\"data row2 col8\" >0.9780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_efcf8_level0_row3\" class=\"row_heading level0 row3\" >ada</th>\n",
       "      <td id=\"T_efcf8_row3_col0\" class=\"data row3 col0\" >Ada Boost Classifier</td>\n",
       "      <td id=\"T_efcf8_row3_col1\" class=\"data row3 col1\" >0.7149</td>\n",
       "      <td id=\"T_efcf8_row3_col2\" class=\"data row3 col2\" >0.7338</td>\n",
       "      <td id=\"T_efcf8_row3_col3\" class=\"data row3 col3\" >0.4724</td>\n",
       "      <td id=\"T_efcf8_row3_col4\" class=\"data row3 col4\" >0.6338</td>\n",
       "      <td id=\"T_efcf8_row3_col5\" class=\"data row3 col5\" >0.5404</td>\n",
       "      <td id=\"T_efcf8_row3_col6\" class=\"data row3 col6\" >0.3402</td>\n",
       "      <td id=\"T_efcf8_row3_col7\" class=\"data row3 col7\" >0.3485</td>\n",
       "      <td id=\"T_efcf8_row3_col8\" class=\"data row3 col8\" >1.3260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_efcf8_level0_row4\" class=\"row_heading level0 row4\" >et</th>\n",
       "      <td id=\"T_efcf8_row4_col0\" class=\"data row4 col0\" >Extra Trees Classifier</td>\n",
       "      <td id=\"T_efcf8_row4_col1\" class=\"data row4 col1\" >0.6976</td>\n",
       "      <td id=\"T_efcf8_row4_col2\" class=\"data row4 col2\" >0.7055</td>\n",
       "      <td id=\"T_efcf8_row4_col3\" class=\"data row4 col3\" >0.2587</td>\n",
       "      <td id=\"T_efcf8_row4_col4\" class=\"data row4 col4\" >0.7049</td>\n",
       "      <td id=\"T_efcf8_row4_col5\" class=\"data row4 col5\" >0.3752</td>\n",
       "      <td id=\"T_efcf8_row4_col6\" class=\"data row4 col6\" >0.2301</td>\n",
       "      <td id=\"T_efcf8_row4_col7\" class=\"data row4 col7\" >0.2810</td>\n",
       "      <td id=\"T_efcf8_row4_col8\" class=\"data row4 col8\" >1.2840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_efcf8_level0_row5\" class=\"row_heading level0 row5\" >lda</th>\n",
       "      <td id=\"T_efcf8_row5_col0\" class=\"data row5 col0\" >Linear Discriminant Analysis</td>\n",
       "      <td id=\"T_efcf8_row5_col1\" class=\"data row5 col1\" >0.6922</td>\n",
       "      <td id=\"T_efcf8_row5_col2\" class=\"data row5 col2\" >0.6832</td>\n",
       "      <td id=\"T_efcf8_row5_col3\" class=\"data row5 col3\" >0.2777</td>\n",
       "      <td id=\"T_efcf8_row5_col4\" class=\"data row5 col4\" >0.6608</td>\n",
       "      <td id=\"T_efcf8_row5_col5\" class=\"data row5 col5\" >0.3882</td>\n",
       "      <td id=\"T_efcf8_row5_col6\" class=\"data row5 col6\" >0.2272</td>\n",
       "      <td id=\"T_efcf8_row5_col7\" class=\"data row5 col7\" >0.2661</td>\n",
       "      <td id=\"T_efcf8_row5_col8\" class=\"data row5 col8\" >0.5060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_efcf8_level0_row6\" class=\"row_heading level0 row6\" >ridge</th>\n",
       "      <td id=\"T_efcf8_row6_col0\" class=\"data row6 col0\" >Ridge Classifier</td>\n",
       "      <td id=\"T_efcf8_row6_col1\" class=\"data row6 col1\" >0.6688</td>\n",
       "      <td id=\"T_efcf8_row6_col2\" class=\"data row6 col2\" >0.0000</td>\n",
       "      <td id=\"T_efcf8_row6_col3\" class=\"data row6 col3\" >0.1673</td>\n",
       "      <td id=\"T_efcf8_row6_col4\" class=\"data row6 col4\" >0.6323</td>\n",
       "      <td id=\"T_efcf8_row6_col5\" class=\"data row6 col5\" >0.2629</td>\n",
       "      <td id=\"T_efcf8_row6_col6\" class=\"data row6 col6\" >0.1343</td>\n",
       "      <td id=\"T_efcf8_row6_col7\" class=\"data row6 col7\" >0.1845</td>\n",
       "      <td id=\"T_efcf8_row6_col8\" class=\"data row6 col8\" >0.4360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_efcf8_level0_row7\" class=\"row_heading level0 row7\" >lr</th>\n",
       "      <td id=\"T_efcf8_row7_col0\" class=\"data row7 col0\" >Logistic Regression</td>\n",
       "      <td id=\"T_efcf8_row7_col1\" class=\"data row7 col1\" >0.6661</td>\n",
       "      <td id=\"T_efcf8_row7_col2\" class=\"data row7 col2\" >0.6408</td>\n",
       "      <td id=\"T_efcf8_row7_col3\" class=\"data row7 col3\" >0.1911</td>\n",
       "      <td id=\"T_efcf8_row7_col4\" class=\"data row7 col4\" >0.5986</td>\n",
       "      <td id=\"T_efcf8_row7_col5\" class=\"data row7 col5\" >0.2880</td>\n",
       "      <td id=\"T_efcf8_row7_col6\" class=\"data row7 col6\" >0.1401</td>\n",
       "      <td id=\"T_efcf8_row7_col7\" class=\"data row7 col7\" >0.1799</td>\n",
       "      <td id=\"T_efcf8_row7_col8\" class=\"data row7 col8\" >1.6020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_efcf8_level0_row8\" class=\"row_heading level0 row8\" >dummy</th>\n",
       "      <td id=\"T_efcf8_row8_col0\" class=\"data row8 col0\" >Dummy Classifier</td>\n",
       "      <td id=\"T_efcf8_row8_col1\" class=\"data row8 col1\" >0.6448</td>\n",
       "      <td id=\"T_efcf8_row8_col2\" class=\"data row8 col2\" >0.5000</td>\n",
       "      <td id=\"T_efcf8_row8_col3\" class=\"data row8 col3\" >0.0000</td>\n",
       "      <td id=\"T_efcf8_row8_col4\" class=\"data row8 col4\" >0.0000</td>\n",
       "      <td id=\"T_efcf8_row8_col5\" class=\"data row8 col5\" >0.0000</td>\n",
       "      <td id=\"T_efcf8_row8_col6\" class=\"data row8 col6\" >0.0000</td>\n",
       "      <td id=\"T_efcf8_row8_col7\" class=\"data row8 col7\" >0.0000</td>\n",
       "      <td id=\"T_efcf8_row8_col8\" class=\"data row8 col8\" >0.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_efcf8_level0_row9\" class=\"row_heading level0 row9\" >dt</th>\n",
       "      <td id=\"T_efcf8_row9_col0\" class=\"data row9 col0\" >Decision Tree Classifier</td>\n",
       "      <td id=\"T_efcf8_row9_col1\" class=\"data row9 col1\" >0.6319</td>\n",
       "      <td id=\"T_efcf8_row9_col2\" class=\"data row9 col2\" >0.6007</td>\n",
       "      <td id=\"T_efcf8_row9_col3\" class=\"data row9 col3\" >0.4932</td>\n",
       "      <td id=\"T_efcf8_row9_col4\" class=\"data row9 col4\" >0.4824</td>\n",
       "      <td id=\"T_efcf8_row9_col5\" class=\"data row9 col5\" >0.4873</td>\n",
       "      <td id=\"T_efcf8_row9_col6\" class=\"data row9 col6\" >0.2004</td>\n",
       "      <td id=\"T_efcf8_row9_col7\" class=\"data row9 col7\" >0.2006</td>\n",
       "      <td id=\"T_efcf8_row9_col8\" class=\"data row9 col8\" >0.6840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_efcf8_level0_row10\" class=\"row_heading level0 row10\" >svm</th>\n",
       "      <td id=\"T_efcf8_row10_col0\" class=\"data row10 col0\" >SVM - Linear Kernel</td>\n",
       "      <td id=\"T_efcf8_row10_col1\" class=\"data row10 col1\" >0.6243</td>\n",
       "      <td id=\"T_efcf8_row10_col2\" class=\"data row10 col2\" >0.0000</td>\n",
       "      <td id=\"T_efcf8_row10_col3\" class=\"data row10 col3\" >0.2740</td>\n",
       "      <td id=\"T_efcf8_row10_col4\" class=\"data row10 col4\" >0.6369</td>\n",
       "      <td id=\"T_efcf8_row10_col5\" class=\"data row10 col5\" >0.2668</td>\n",
       "      <td id=\"T_efcf8_row10_col6\" class=\"data row10 col6\" >0.0981</td>\n",
       "      <td id=\"T_efcf8_row10_col7\" class=\"data row10 col7\" >0.1559</td>\n",
       "      <td id=\"T_efcf8_row10_col8\" class=\"data row10 col8\" >0.7560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_efcf8_level0_row11\" class=\"row_heading level0 row11\" >knn</th>\n",
       "      <td id=\"T_efcf8_row11_col0\" class=\"data row11 col0\" >K Neighbors Classifier</td>\n",
       "      <td id=\"T_efcf8_row11_col1\" class=\"data row11 col1\" >0.6140</td>\n",
       "      <td id=\"T_efcf8_row11_col2\" class=\"data row11 col2\" >0.5741</td>\n",
       "      <td id=\"T_efcf8_row11_col3\" class=\"data row11 col3\" >0.3370</td>\n",
       "      <td id=\"T_efcf8_row11_col4\" class=\"data row11 col4\" >0.4434</td>\n",
       "      <td id=\"T_efcf8_row11_col5\" class=\"data row11 col5\" >0.3816</td>\n",
       "      <td id=\"T_efcf8_row11_col6\" class=\"data row11 col6\" >0.1093</td>\n",
       "      <td id=\"T_efcf8_row11_col7\" class=\"data row11 col7\" >0.1118</td>\n",
       "      <td id=\"T_efcf8_row11_col8\" class=\"data row11 col8\" >1.3980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_efcf8_level0_row12\" class=\"row_heading level0 row12\" >qda</th>\n",
       "      <td id=\"T_efcf8_row12_col0\" class=\"data row12 col0\" >Quadratic Discriminant Analysis</td>\n",
       "      <td id=\"T_efcf8_row12_col1\" class=\"data row12 col1\" >0.4366</td>\n",
       "      <td id=\"T_efcf8_row12_col2\" class=\"data row12 col2\" >0.6377</td>\n",
       "      <td id=\"T_efcf8_row12_col3\" class=\"data row12 col3\" >0.8915</td>\n",
       "      <td id=\"T_efcf8_row12_col4\" class=\"data row12 col4\" >0.3824</td>\n",
       "      <td id=\"T_efcf8_row12_col5\" class=\"data row12 col5\" >0.5301</td>\n",
       "      <td id=\"T_efcf8_row12_col6\" class=\"data row12 col6\" >0.0644</td>\n",
       "      <td id=\"T_efcf8_row12_col7\" class=\"data row12 col7\" >0.0954</td>\n",
       "      <td id=\"T_efcf8_row12_col8\" class=\"data row12 col8\" >0.4300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_efcf8_level0_row13\" class=\"row_heading level0 row13\" >nb</th>\n",
       "      <td id=\"T_efcf8_row13_col0\" class=\"data row13 col0\" >Naive Bayes</td>\n",
       "      <td id=\"T_efcf8_row13_col1\" class=\"data row13 col1\" >0.3920</td>\n",
       "      <td id=\"T_efcf8_row13_col2\" class=\"data row13 col2\" >0.5744</td>\n",
       "      <td id=\"T_efcf8_row13_col3\" class=\"data row13 col3\" >0.9193</td>\n",
       "      <td id=\"T_efcf8_row13_col4\" class=\"data row13 col4\" >0.3623</td>\n",
       "      <td id=\"T_efcf8_row13_col5\" class=\"data row13 col5\" >0.5173</td>\n",
       "      <td id=\"T_efcf8_row13_col6\" class=\"data row13 col6\" >0.0173</td>\n",
       "      <td id=\"T_efcf8_row13_col7\" class=\"data row13 col7\" >0.0203</td>\n",
       "      <td id=\"T_efcf8_row13_col8\" class=\"data row13 col8\" >0.9340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fbdbd181890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing:   0%|          | 0/61 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = compare_models(fold=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "fabd9b9a-9030-4f08-a5ed-ac6fc59435fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_68670_row10_col0, #T_68670_row10_col1, #T_68670_row10_col2, #T_68670_row10_col3, #T_68670_row10_col4, #T_68670_row10_col5, #T_68670_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_68670_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_68670_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_68670_row0_col0\" class=\"data row0 col0\" >0.7301</td>\n",
       "      <td id=\"T_68670_row0_col1\" class=\"data row0 col1\" >0.7802</td>\n",
       "      <td id=\"T_68670_row0_col2\" class=\"data row0 col2\" >0.4185</td>\n",
       "      <td id=\"T_68670_row0_col3\" class=\"data row0 col3\" >0.7013</td>\n",
       "      <td id=\"T_68670_row0_col4\" class=\"data row0 col4\" >0.5241</td>\n",
       "      <td id=\"T_68670_row0_col5\" class=\"data row0 col5\" >0.3522</td>\n",
       "      <td id=\"T_68670_row0_col6\" class=\"data row0 col6\" >0.3750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68670_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_68670_row1_col0\" class=\"data row1 col0\" >0.7144</td>\n",
       "      <td id=\"T_68670_row1_col1\" class=\"data row1 col1\" >0.7581</td>\n",
       "      <td id=\"T_68670_row1_col2\" class=\"data row1 col2\" >0.3932</td>\n",
       "      <td id=\"T_68670_row1_col3\" class=\"data row1 col3\" >0.6660</td>\n",
       "      <td id=\"T_68670_row1_col4\" class=\"data row1 col4\" >0.4944</td>\n",
       "      <td id=\"T_68670_row1_col5\" class=\"data row1 col5\" >0.3134</td>\n",
       "      <td id=\"T_68670_row1_col6\" class=\"data row1 col6\" >0.3345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68670_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_68670_row2_col0\" class=\"data row2 col0\" >0.7140</td>\n",
       "      <td id=\"T_68670_row2_col1\" class=\"data row2 col1\" >0.7249</td>\n",
       "      <td id=\"T_68670_row2_col2\" class=\"data row2 col2\" >0.4058</td>\n",
       "      <td id=\"T_68670_row2_col3\" class=\"data row2 col3\" >0.6578</td>\n",
       "      <td id=\"T_68670_row2_col4\" class=\"data row2 col4\" >0.5020</td>\n",
       "      <td id=\"T_68670_row2_col5\" class=\"data row2 col5\" >0.3168</td>\n",
       "      <td id=\"T_68670_row2_col6\" class=\"data row2 col6\" >0.3350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68670_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_68670_row3_col0\" class=\"data row3 col0\" >0.7306</td>\n",
       "      <td id=\"T_68670_row3_col1\" class=\"data row3 col1\" >0.7467</td>\n",
       "      <td id=\"T_68670_row3_col2\" class=\"data row3 col2\" >0.4753</td>\n",
       "      <td id=\"T_68670_row3_col3\" class=\"data row3 col3\" >0.6702</td>\n",
       "      <td id=\"T_68670_row3_col4\" class=\"data row3 col4\" >0.5562</td>\n",
       "      <td id=\"T_68670_row3_col5\" class=\"data row3 col5\" >0.3707</td>\n",
       "      <td id=\"T_68670_row3_col6\" class=\"data row3 col6\" >0.3820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68670_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_68670_row4_col0\" class=\"data row4 col0\" >0.7616</td>\n",
       "      <td id=\"T_68670_row4_col1\" class=\"data row4 col1\" >0.7603</td>\n",
       "      <td id=\"T_68670_row4_col2\" class=\"data row4 col2\" >0.4766</td>\n",
       "      <td id=\"T_68670_row4_col3\" class=\"data row4 col3\" >0.7632</td>\n",
       "      <td id=\"T_68670_row4_col4\" class=\"data row4 col4\" >0.5868</td>\n",
       "      <td id=\"T_68670_row4_col5\" class=\"data row4 col5\" >0.4315</td>\n",
       "      <td id=\"T_68670_row4_col6\" class=\"data row4 col6\" >0.4551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68670_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_68670_row5_col0\" class=\"data row5 col0\" >0.7601</td>\n",
       "      <td id=\"T_68670_row5_col1\" class=\"data row5 col1\" >0.7683</td>\n",
       "      <td id=\"T_68670_row5_col2\" class=\"data row5 col2\" >0.5076</td>\n",
       "      <td id=\"T_68670_row5_col3\" class=\"data row5 col3\" >0.7344</td>\n",
       "      <td id=\"T_68670_row5_col4\" class=\"data row5 col4\" >0.6003</td>\n",
       "      <td id=\"T_68670_row5_col5\" class=\"data row5 col5\" >0.4370</td>\n",
       "      <td id=\"T_68670_row5_col6\" class=\"data row5 col6\" >0.4522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68670_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_68670_row6_col0\" class=\"data row6 col0\" >0.7345</td>\n",
       "      <td id=\"T_68670_row6_col1\" class=\"data row6 col1\" >0.7643</td>\n",
       "      <td id=\"T_68670_row6_col2\" class=\"data row6 col2\" >0.3582</td>\n",
       "      <td id=\"T_68670_row6_col3\" class=\"data row6 col3\" >0.7711</td>\n",
       "      <td id=\"T_68670_row6_col4\" class=\"data row6 col4\" >0.4892</td>\n",
       "      <td id=\"T_68670_row6_col5\" class=\"data row6 col5\" >0.3408</td>\n",
       "      <td id=\"T_68670_row6_col6\" class=\"data row6 col6\" >0.3865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68670_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_68670_row7_col0\" class=\"data row7 col0\" >0.7502</td>\n",
       "      <td id=\"T_68670_row7_col1\" class=\"data row7 col1\" >0.7830</td>\n",
       "      <td id=\"T_68670_row7_col2\" class=\"data row7 col2\" >0.5019</td>\n",
       "      <td id=\"T_68670_row7_col3\" class=\"data row7 col3\" >0.7102</td>\n",
       "      <td id=\"T_68670_row7_col4\" class=\"data row7 col4\" >0.5881</td>\n",
       "      <td id=\"T_68670_row7_col5\" class=\"data row7 col5\" >0.4164</td>\n",
       "      <td id=\"T_68670_row7_col6\" class=\"data row7 col6\" >0.4293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68670_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_68670_row8_col0\" class=\"data row8 col0\" >0.7282</td>\n",
       "      <td id=\"T_68670_row8_col1\" class=\"data row8 col1\" >0.7501</td>\n",
       "      <td id=\"T_68670_row8_col2\" class=\"data row8 col2\" >0.4867</td>\n",
       "      <td id=\"T_68670_row8_col3\" class=\"data row8 col3\" >0.6592</td>\n",
       "      <td id=\"T_68670_row8_col4\" class=\"data row8 col4\" >0.5600</td>\n",
       "      <td id=\"T_68670_row8_col5\" class=\"data row8 col5\" >0.3698</td>\n",
       "      <td id=\"T_68670_row8_col6\" class=\"data row8 col6\" >0.3787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68670_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_68670_row9_col0\" class=\"data row9 col0\" >0.7412</td>\n",
       "      <td id=\"T_68670_row9_col1\" class=\"data row9 col1\" >0.7580</td>\n",
       "      <td id=\"T_68670_row9_col2\" class=\"data row9 col2\" >0.5006</td>\n",
       "      <td id=\"T_68670_row9_col3\" class=\"data row9 col3\" >0.6863</td>\n",
       "      <td id=\"T_68670_row9_col4\" class=\"data row9 col4\" >0.5789</td>\n",
       "      <td id=\"T_68670_row9_col5\" class=\"data row9 col5\" >0.3987</td>\n",
       "      <td id=\"T_68670_row9_col6\" class=\"data row9 col6\" >0.4090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68670_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_68670_row10_col0\" class=\"data row10 col0\" >0.7365</td>\n",
       "      <td id=\"T_68670_row10_col1\" class=\"data row10 col1\" >0.7594</td>\n",
       "      <td id=\"T_68670_row10_col2\" class=\"data row10 col2\" >0.4524</td>\n",
       "      <td id=\"T_68670_row10_col3\" class=\"data row10 col3\" >0.7020</td>\n",
       "      <td id=\"T_68670_row10_col4\" class=\"data row10 col4\" >0.5480</td>\n",
       "      <td id=\"T_68670_row10_col5\" class=\"data row10 col5\" >0.3747</td>\n",
       "      <td id=\"T_68670_row10_col6\" class=\"data row10 col6\" >0.3937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68670_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_68670_row11_col0\" class=\"data row11 col0\" >0.0159</td>\n",
       "      <td id=\"T_68670_row11_col1\" class=\"data row11 col1\" >0.0159</td>\n",
       "      <td id=\"T_68670_row11_col2\" class=\"data row11 col2\" >0.0508</td>\n",
       "      <td id=\"T_68670_row11_col3\" class=\"data row11 col3\" >0.0400</td>\n",
       "      <td id=\"T_68670_row11_col4\" class=\"data row11 col4\" >0.0400</td>\n",
       "      <td id=\"T_68670_row11_col5\" class=\"data row11 col5\" >0.0427</td>\n",
       "      <td id=\"T_68670_row11_col6\" class=\"data row11 col6\" >0.0405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fbdbd17cd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbc = create_model('gbc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "1942c169-a4f1-4a5e-951e-17354e43ce79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_74edd_row10_col0, #T_74edd_row10_col1, #T_74edd_row10_col2, #T_74edd_row10_col3, #T_74edd_row10_col4, #T_74edd_row10_col5, #T_74edd_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_74edd_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_74edd_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_74edd_row0_col0\" class=\"data row0 col0\" >0.7238</td>\n",
       "      <td id=\"T_74edd_row0_col1\" class=\"data row0 col1\" >0.7833</td>\n",
       "      <td id=\"T_74edd_row0_col2\" class=\"data row0 col2\" >0.3742</td>\n",
       "      <td id=\"T_74edd_row0_col3\" class=\"data row0 col3\" >0.7115</td>\n",
       "      <td id=\"T_74edd_row0_col4\" class=\"data row0 col4\" >0.4905</td>\n",
       "      <td id=\"T_74edd_row0_col5\" class=\"data row0 col5\" >0.3253</td>\n",
       "      <td id=\"T_74edd_row0_col6\" class=\"data row0 col6\" >0.3569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_74edd_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_74edd_row1_col0\" class=\"data row1 col0\" >0.7319</td>\n",
       "      <td id=\"T_74edd_row1_col1\" class=\"data row1 col1\" >0.7662</td>\n",
       "      <td id=\"T_74edd_row1_col2\" class=\"data row1 col2\" >0.4083</td>\n",
       "      <td id=\"T_74edd_row1_col3\" class=\"data row1 col3\" >0.7146</td>\n",
       "      <td id=\"T_74edd_row1_col4\" class=\"data row1 col4\" >0.5197</td>\n",
       "      <td id=\"T_74edd_row1_col5\" class=\"data row1 col5\" >0.3524</td>\n",
       "      <td id=\"T_74edd_row1_col6\" class=\"data row1 col6\" >0.3790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_74edd_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_74edd_row2_col0\" class=\"data row2 col0\" >0.7113</td>\n",
       "      <td id=\"T_74edd_row2_col1\" class=\"data row2 col1\" >0.6979</td>\n",
       "      <td id=\"T_74edd_row2_col2\" class=\"data row2 col2\" >0.3401</td>\n",
       "      <td id=\"T_74edd_row2_col3\" class=\"data row2 col3\" >0.6897</td>\n",
       "      <td id=\"T_74edd_row2_col4\" class=\"data row2 col4\" >0.4555</td>\n",
       "      <td id=\"T_74edd_row2_col5\" class=\"data row2 col5\" >0.2887</td>\n",
       "      <td id=\"T_74edd_row2_col6\" class=\"data row2 col6\" >0.3221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_74edd_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_74edd_row3_col0\" class=\"data row3 col0\" >0.7346</td>\n",
       "      <td id=\"T_74edd_row3_col1\" class=\"data row3 col1\" >0.7608</td>\n",
       "      <td id=\"T_74edd_row3_col2\" class=\"data row3 col2\" >0.3780</td>\n",
       "      <td id=\"T_74edd_row3_col3\" class=\"data row3 col3\" >0.7513</td>\n",
       "      <td id=\"T_74edd_row3_col4\" class=\"data row3 col4\" >0.5029</td>\n",
       "      <td id=\"T_74edd_row3_col5\" class=\"data row3 col5\" >0.3479</td>\n",
       "      <td id=\"T_74edd_row3_col6\" class=\"data row3 col6\" >0.3861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_74edd_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_74edd_row4_col0\" class=\"data row4 col0\" >0.7485</td>\n",
       "      <td id=\"T_74edd_row4_col1\" class=\"data row4 col1\" >0.7737</td>\n",
       "      <td id=\"T_74edd_row4_col2\" class=\"data row4 col2\" >0.3906</td>\n",
       "      <td id=\"T_74edd_row4_col3\" class=\"data row4 col3\" >0.7984</td>\n",
       "      <td id=\"T_74edd_row4_col4\" class=\"data row4 col4\" >0.5246</td>\n",
       "      <td id=\"T_74edd_row4_col5\" class=\"data row4 col5\" >0.3799</td>\n",
       "      <td id=\"T_74edd_row4_col6\" class=\"data row4 col6\" >0.4248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_74edd_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_74edd_row5_col0\" class=\"data row5 col0\" >0.7821</td>\n",
       "      <td id=\"T_74edd_row5_col1\" class=\"data row5 col1\" >0.8088</td>\n",
       "      <td id=\"T_74edd_row5_col2\" class=\"data row5 col2\" >0.4544</td>\n",
       "      <td id=\"T_74edd_row5_col3\" class=\"data row5 col3\" >0.8692</td>\n",
       "      <td id=\"T_74edd_row5_col4\" class=\"data row5 col4\" >0.5968</td>\n",
       "      <td id=\"T_74edd_row5_col5\" class=\"data row5 col5\" >0.4669</td>\n",
       "      <td id=\"T_74edd_row5_col6\" class=\"data row5 col6\" >0.5131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_74edd_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_74edd_row6_col0\" class=\"data row6 col0\" >0.7269</td>\n",
       "      <td id=\"T_74edd_row6_col1\" class=\"data row6 col1\" >0.7560</td>\n",
       "      <td id=\"T_74edd_row6_col2\" class=\"data row6 col2\" >0.2873</td>\n",
       "      <td id=\"T_74edd_row6_col3\" class=\"data row6 col3\" >0.8346</td>\n",
       "      <td id=\"T_74edd_row6_col4\" class=\"data row6 col4\" >0.4275</td>\n",
       "      <td id=\"T_74edd_row6_col5\" class=\"data row6 col5\" >0.3003</td>\n",
       "      <td id=\"T_74edd_row6_col6\" class=\"data row6 col6\" >0.3740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_74edd_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_74edd_row7_col0\" class=\"data row7 col0\" >0.7457</td>\n",
       "      <td id=\"T_74edd_row7_col1\" class=\"data row7 col1\" >0.7849</td>\n",
       "      <td id=\"T_74edd_row7_col2\" class=\"data row7 col2\" >0.4210</td>\n",
       "      <td id=\"T_74edd_row7_col3\" class=\"data row7 col3\" >0.7551</td>\n",
       "      <td id=\"T_74edd_row7_col4\" class=\"data row7 col4\" >0.5406</td>\n",
       "      <td id=\"T_74edd_row7_col5\" class=\"data row7 col5\" >0.3838</td>\n",
       "      <td id=\"T_74edd_row7_col6\" class=\"data row7 col6\" >0.4152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_74edd_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_74edd_row8_col0\" class=\"data row8 col0\" >0.7305</td>\n",
       "      <td id=\"T_74edd_row8_col1\" class=\"data row8 col1\" >0.7511</td>\n",
       "      <td id=\"T_74edd_row8_col2\" class=\"data row8 col2\" >0.4260</td>\n",
       "      <td id=\"T_74edd_row8_col3\" class=\"data row8 col3\" >0.6977</td>\n",
       "      <td id=\"T_74edd_row8_col4\" class=\"data row8 col4\" >0.5290</td>\n",
       "      <td id=\"T_74edd_row8_col5\" class=\"data row8 col5\" >0.3553</td>\n",
       "      <td id=\"T_74edd_row8_col6\" class=\"data row8 col6\" >0.3766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_74edd_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_74edd_row9_col0\" class=\"data row9 col0\" >0.7417</td>\n",
       "      <td id=\"T_74edd_row9_col1\" class=\"data row9 col1\" >0.7580</td>\n",
       "      <td id=\"T_74edd_row9_col2\" class=\"data row9 col2\" >0.4399</td>\n",
       "      <td id=\"T_74edd_row9_col3\" class=\"data row9 col3\" >0.7250</td>\n",
       "      <td id=\"T_74edd_row9_col4\" class=\"data row9 col4\" >0.5476</td>\n",
       "      <td id=\"T_74edd_row9_col5\" class=\"data row9 col5\" >0.3816</td>\n",
       "      <td id=\"T_74edd_row9_col6\" class=\"data row9 col6\" >0.4050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_74edd_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_74edd_row10_col0\" class=\"data row10 col0\" >0.7377</td>\n",
       "      <td id=\"T_74edd_row10_col1\" class=\"data row10 col1\" >0.7641</td>\n",
       "      <td id=\"T_74edd_row10_col2\" class=\"data row10 col2\" >0.3920</td>\n",
       "      <td id=\"T_74edd_row10_col3\" class=\"data row10 col3\" >0.7547</td>\n",
       "      <td id=\"T_74edd_row10_col4\" class=\"data row10 col4\" >0.5135</td>\n",
       "      <td id=\"T_74edd_row10_col5\" class=\"data row10 col5\" >0.3582</td>\n",
       "      <td id=\"T_74edd_row10_col6\" class=\"data row10 col6\" >0.3953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_74edd_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_74edd_row11_col0\" class=\"data row11 col0\" >0.0181</td>\n",
       "      <td id=\"T_74edd_row11_col1\" class=\"data row11 col1\" >0.0275</td>\n",
       "      <td id=\"T_74edd_row11_col2\" class=\"data row11 col2\" >0.0476</td>\n",
       "      <td id=\"T_74edd_row11_col3\" class=\"data row11 col3\" >0.0577</td>\n",
       "      <td id=\"T_74edd_row11_col4\" class=\"data row11 col4\" >0.0454</td>\n",
       "      <td id=\"T_74edd_row11_col5\" class=\"data row11 col5\" >0.0479</td>\n",
       "      <td id=\"T_74edd_row11_col6\" class=\"data row11 col6\" >0.0482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fbdbde04390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing:   0%|          | 0/7 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
     ]
    }
   ],
   "source": [
    "tuned_gbc = tune_model(gbc, optimize = 'AUC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d996cb17-2cee-4912-ad65-7e8a9de96d69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_gbc, plot = 'confusion_matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6fcd583a-48eb-410a-9378-937203802668",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(gbc, plot = 'feature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "4e9245cd-7704-4b96-97a6-a3f7e1c4f23f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_f1961_row10_col0, #T_f1961_row10_col1, #T_f1961_row10_col2, #T_f1961_row10_col3, #T_f1961_row10_col4, #T_f1961_row10_col5, #T_f1961_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_f1961_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f1961_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_f1961_row0_col0\" class=\"data row0 col0\" >0.7391</td>\n",
       "      <td id=\"T_f1961_row0_col1\" class=\"data row0 col1\" >0.7676</td>\n",
       "      <td id=\"T_f1961_row0_col2\" class=\"data row0 col2\" >0.4589</td>\n",
       "      <td id=\"T_f1961_row0_col3\" class=\"data row0 col3\" >0.7035</td>\n",
       "      <td id=\"T_f1961_row0_col4\" class=\"data row0 col4\" >0.5555</td>\n",
       "      <td id=\"T_f1961_row0_col5\" class=\"data row0 col5\" >0.3822</td>\n",
       "      <td id=\"T_f1961_row0_col6\" class=\"data row0 col6\" >0.3997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f1961_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_f1961_row1_col0\" class=\"data row1 col0\" >0.7256</td>\n",
       "      <td id=\"T_f1961_row1_col1\" class=\"data row1 col1\" >0.7764</td>\n",
       "      <td id=\"T_f1961_row1_col2\" class=\"data row1 col2\" >0.3729</td>\n",
       "      <td id=\"T_f1961_row1_col3\" class=\"data row1 col3\" >0.7195</td>\n",
       "      <td id=\"T_f1961_row1_col4\" class=\"data row1 col4\" >0.4913</td>\n",
       "      <td id=\"T_f1961_row1_col5\" class=\"data row1 col5\" >0.3284</td>\n",
       "      <td id=\"T_f1961_row1_col6\" class=\"data row1 col6\" >0.3616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f1961_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_f1961_row2_col0\" class=\"data row2 col0\" >0.7059</td>\n",
       "      <td id=\"T_f1961_row2_col1\" class=\"data row2 col1\" >0.7282</td>\n",
       "      <td id=\"T_f1961_row2_col2\" class=\"data row2 col2\" >0.3793</td>\n",
       "      <td id=\"T_f1961_row2_col3\" class=\"data row2 col3\" >0.6466</td>\n",
       "      <td id=\"T_f1961_row2_col4\" class=\"data row2 col4\" >0.4781</td>\n",
       "      <td id=\"T_f1961_row2_col5\" class=\"data row2 col5\" >0.2922</td>\n",
       "      <td id=\"T_f1961_row2_col6\" class=\"data row2 col6\" >0.3123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f1961_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_f1961_row3_col0\" class=\"data row3 col0\" >0.7405</td>\n",
       "      <td id=\"T_f1961_row3_col1\" class=\"data row3 col1\" >0.7470</td>\n",
       "      <td id=\"T_f1961_row3_col2\" class=\"data row3 col2\" >0.4716</td>\n",
       "      <td id=\"T_f1961_row3_col3\" class=\"data row3 col3\" >0.6998</td>\n",
       "      <td id=\"T_f1961_row3_col4\" class=\"data row3 col4\" >0.5634</td>\n",
       "      <td id=\"T_f1961_row3_col5\" class=\"data row3 col5\" >0.3886</td>\n",
       "      <td id=\"T_f1961_row3_col6\" class=\"data row3 col6\" >0.4039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f1961_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_f1961_row4_col0\" class=\"data row4 col0\" >0.7687</td>\n",
       "      <td id=\"T_f1961_row4_col1\" class=\"data row4 col1\" >0.7600</td>\n",
       "      <td id=\"T_f1961_row4_col2\" class=\"data row4 col2\" >0.5006</td>\n",
       "      <td id=\"T_f1961_row4_col3\" class=\"data row4 col3\" >0.7674</td>\n",
       "      <td id=\"T_f1961_row4_col4\" class=\"data row4 col4\" >0.6060</td>\n",
       "      <td id=\"T_f1961_row4_col5\" class=\"data row4 col5\" >0.4524</td>\n",
       "      <td id=\"T_f1961_row4_col6\" class=\"data row4 col6\" >0.4731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f1961_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_f1961_row5_col0\" class=\"data row5 col0\" >0.7803</td>\n",
       "      <td id=\"T_f1961_row5_col1\" class=\"data row5 col1\" >0.8016</td>\n",
       "      <td id=\"T_f1961_row5_col2\" class=\"data row5 col2\" >0.4861</td>\n",
       "      <td id=\"T_f1961_row5_col3\" class=\"data row5 col3\" >0.8223</td>\n",
       "      <td id=\"T_f1961_row5_col4\" class=\"data row5 col4\" >0.6110</td>\n",
       "      <td id=\"T_f1961_row5_col5\" class=\"data row5 col5\" >0.4717</td>\n",
       "      <td id=\"T_f1961_row5_col6\" class=\"data row5 col6\" >0.5033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f1961_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_f1961_row6_col0\" class=\"data row6 col0\" >0.7381</td>\n",
       "      <td id=\"T_f1961_row6_col1\" class=\"data row6 col1\" >0.7670</td>\n",
       "      <td id=\"T_f1961_row6_col2\" class=\"data row6 col2\" >0.3456</td>\n",
       "      <td id=\"T_f1961_row6_col3\" class=\"data row6 col3\" >0.8053</td>\n",
       "      <td id=\"T_f1961_row6_col4\" class=\"data row6 col4\" >0.4836</td>\n",
       "      <td id=\"T_f1961_row6_col5\" class=\"data row6 col5\" >0.3437</td>\n",
       "      <td id=\"T_f1961_row6_col6\" class=\"data row6 col6\" >0.3990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f1961_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_f1961_row7_col0\" class=\"data row7 col0\" >0.7547</td>\n",
       "      <td id=\"T_f1961_row7_col1\" class=\"data row7 col1\" >0.7840</td>\n",
       "      <td id=\"T_f1961_row7_col2\" class=\"data row7 col2\" >0.4753</td>\n",
       "      <td id=\"T_f1961_row7_col3\" class=\"data row7 col3\" >0.7416</td>\n",
       "      <td id=\"T_f1961_row7_col4\" class=\"data row7 col4\" >0.5794</td>\n",
       "      <td id=\"T_f1961_row7_col5\" class=\"data row7 col5\" >0.4177</td>\n",
       "      <td id=\"T_f1961_row7_col6\" class=\"data row7 col6\" >0.4383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f1961_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_f1961_row8_col0\" class=\"data row8 col0\" >0.7305</td>\n",
       "      <td id=\"T_f1961_row8_col1\" class=\"data row8 col1\" >0.7481</td>\n",
       "      <td id=\"T_f1961_row8_col2\" class=\"data row8 col2\" >0.4564</td>\n",
       "      <td id=\"T_f1961_row8_col3\" class=\"data row8 col3\" >0.6798</td>\n",
       "      <td id=\"T_f1961_row8_col4\" class=\"data row8 col4\" >0.5461</td>\n",
       "      <td id=\"T_f1961_row8_col5\" class=\"data row8 col5\" >0.3648</td>\n",
       "      <td id=\"T_f1961_row8_col6\" class=\"data row8 col6\" >0.3795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f1961_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_f1961_row9_col0\" class=\"data row9 col0\" >0.7421</td>\n",
       "      <td id=\"T_f1961_row9_col1\" class=\"data row9 col1\" >0.7582</td>\n",
       "      <td id=\"T_f1961_row9_col2\" class=\"data row9 col2\" >0.4918</td>\n",
       "      <td id=\"T_f1961_row9_col3\" class=\"data row9 col3\" >0.6934</td>\n",
       "      <td id=\"T_f1961_row9_col4\" class=\"data row9 col4\" >0.5754</td>\n",
       "      <td id=\"T_f1961_row9_col5\" class=\"data row9 col5\" >0.3979</td>\n",
       "      <td id=\"T_f1961_row9_col6\" class=\"data row9 col6\" >0.4100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f1961_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_f1961_row10_col0\" class=\"data row10 col0\" >0.7426</td>\n",
       "      <td id=\"T_f1961_row10_col1\" class=\"data row10 col1\" >0.7638</td>\n",
       "      <td id=\"T_f1961_row10_col2\" class=\"data row10 col2\" >0.4438</td>\n",
       "      <td id=\"T_f1961_row10_col3\" class=\"data row10 col3\" >0.7279</td>\n",
       "      <td id=\"T_f1961_row10_col4\" class=\"data row10 col4\" >0.5490</td>\n",
       "      <td id=\"T_f1961_row10_col5\" class=\"data row10 col5\" >0.3840</td>\n",
       "      <td id=\"T_f1961_row10_col6\" class=\"data row10 col6\" >0.4081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f1961_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_f1961_row11_col0\" class=\"data row11 col0\" >0.0202</td>\n",
       "      <td id=\"T_f1961_row11_col1\" class=\"data row11 col1\" >0.0196</td>\n",
       "      <td id=\"T_f1961_row11_col2\" class=\"data row11 col2\" >0.0532</td>\n",
       "      <td id=\"T_f1961_row11_col3\" class=\"data row11 col3\" >0.0531</td>\n",
       "      <td id=\"T_f1961_row11_col4\" class=\"data row11 col4\" >0.0465</td>\n",
       "      <td id=\"T_f1961_row11_col5\" class=\"data row11 col5\" >0.0522</td>\n",
       "      <td id=\"T_f1961_row11_col6\" class=\"data row11 col6\" >0.0515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fbdbcc96a10>"
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       "Processing:   0%|          | 0/4 [00:00<?, ?it/s]"
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    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_5ea09_row10_col0, #T_5ea09_row10_col1, #T_5ea09_row10_col2, #T_5ea09_row10_col3, #T_5ea09_row10_col4, #T_5ea09_row10_col5, #T_5ea09_row10_col6 {\n",
       "  background: yellow;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_5ea09_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th class=\"col_heading level0 col1\" >AUC</th>\n",
       "      <th class=\"col_heading level0 col2\" >Recall</th>\n",
       "      <th class=\"col_heading level0 col3\" >Prec.</th>\n",
       "      <th class=\"col_heading level0 col4\" >F1</th>\n",
       "      <th class=\"col_heading level0 col5\" >Kappa</th>\n",
       "      <th class=\"col_heading level0 col6\" >MCC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Fold</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_5ea09_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_5ea09_row0_col0\" class=\"data row0 col0\" >0.7063</td>\n",
       "      <td id=\"T_5ea09_row0_col1\" class=\"data row0 col1\" >0.7437</td>\n",
       "      <td id=\"T_5ea09_row0_col2\" class=\"data row0 col2\" >0.3515</td>\n",
       "      <td id=\"T_5ea09_row0_col3\" class=\"data row0 col3\" >0.6635</td>\n",
       "      <td id=\"T_5ea09_row0_col4\" class=\"data row0 col4\" >0.4595</td>\n",
       "      <td id=\"T_5ea09_row0_col5\" class=\"data row0 col5\" >0.2832</td>\n",
       "      <td id=\"T_5ea09_row0_col6\" class=\"data row0 col6\" >0.3101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5ea09_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_5ea09_row1_col0\" class=\"data row1 col0\" >0.7454</td>\n",
       "      <td id=\"T_5ea09_row1_col1\" class=\"data row1 col1\" >0.7490</td>\n",
       "      <td id=\"T_5ea09_row1_col2\" class=\"data row1 col2\" >0.3982</td>\n",
       "      <td id=\"T_5ea09_row1_col3\" class=\"data row1 col3\" >0.7759</td>\n",
       "      <td id=\"T_5ea09_row1_col4\" class=\"data row1 col4\" >0.5263</td>\n",
       "      <td id=\"T_5ea09_row1_col5\" class=\"data row1 col5\" >0.3760</td>\n",
       "      <td id=\"T_5ea09_row1_col6\" class=\"data row1 col6\" >0.4151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5ea09_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_5ea09_row2_col0\" class=\"data row2 col0\" >0.7126</td>\n",
       "      <td id=\"T_5ea09_row2_col1\" class=\"data row2 col1\" >0.7105</td>\n",
       "      <td id=\"T_5ea09_row2_col2\" class=\"data row2 col2\" >0.3742</td>\n",
       "      <td id=\"T_5ea09_row2_col3\" class=\"data row2 col3\" >0.6712</td>\n",
       "      <td id=\"T_5ea09_row2_col4\" class=\"data row2 col4\" >0.4805</td>\n",
       "      <td id=\"T_5ea09_row2_col5\" class=\"data row2 col5\" >0.3034</td>\n",
       "      <td id=\"T_5ea09_row2_col6\" class=\"data row2 col6\" >0.3281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5ea09_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_5ea09_row3_col0\" class=\"data row3 col0\" >0.7432</td>\n",
       "      <td id=\"T_5ea09_row3_col1\" class=\"data row3 col1\" >0.7545</td>\n",
       "      <td id=\"T_5ea09_row3_col2\" class=\"data row3 col2\" >0.4046</td>\n",
       "      <td id=\"T_5ea09_row3_col3\" class=\"data row3 col3\" >0.7601</td>\n",
       "      <td id=\"T_5ea09_row3_col4\" class=\"data row3 col4\" >0.5281</td>\n",
       "      <td id=\"T_5ea09_row3_col5\" class=\"data row3 col5\" >0.3734</td>\n",
       "      <td id=\"T_5ea09_row3_col6\" class=\"data row3 col6\" >0.4085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5ea09_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_5ea09_row4_col0\" class=\"data row4 col0\" >0.7629</td>\n",
       "      <td id=\"T_5ea09_row4_col1\" class=\"data row4 col1\" >0.7779</td>\n",
       "      <td id=\"T_5ea09_row4_col2\" class=\"data row4 col2\" >0.4766</td>\n",
       "      <td id=\"T_5ea09_row4_col3\" class=\"data row4 col3\" >0.7678</td>\n",
       "      <td id=\"T_5ea09_row4_col4\" class=\"data row4 col4\" >0.5881</td>\n",
       "      <td id=\"T_5ea09_row4_col5\" class=\"data row4 col5\" >0.4342</td>\n",
       "      <td id=\"T_5ea09_row4_col6\" class=\"data row4 col6\" >0.4585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5ea09_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_5ea09_row5_col0\" class=\"data row5 col0\" >0.7664</td>\n",
       "      <td id=\"T_5ea09_row5_col1\" class=\"data row5 col1\" >0.7760</td>\n",
       "      <td id=\"T_5ea09_row5_col2\" class=\"data row5 col2\" >0.4418</td>\n",
       "      <td id=\"T_5ea09_row5_col3\" class=\"data row5 col3\" >0.8154</td>\n",
       "      <td id=\"T_5ea09_row5_col4\" class=\"data row5 col4\" >0.5731</td>\n",
       "      <td id=\"T_5ea09_row5_col5\" class=\"data row5 col5\" >0.4312</td>\n",
       "      <td id=\"T_5ea09_row5_col6\" class=\"data row5 col6\" >0.4696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5ea09_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_5ea09_row6_col0\" class=\"data row6 col0\" >0.7278</td>\n",
       "      <td id=\"T_5ea09_row6_col1\" class=\"data row6 col1\" >0.7597</td>\n",
       "      <td id=\"T_5ea09_row6_col2\" class=\"data row6 col2\" >0.3063</td>\n",
       "      <td id=\"T_5ea09_row6_col3\" class=\"data row6 col3\" >0.8067</td>\n",
       "      <td id=\"T_5ea09_row6_col4\" class=\"data row6 col4\" >0.4440</td>\n",
       "      <td id=\"T_5ea09_row6_col5\" class=\"data row6 col5\" >0.3091</td>\n",
       "      <td id=\"T_5ea09_row6_col6\" class=\"data row6 col6\" >0.3726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5ea09_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_5ea09_row7_col0\" class=\"data row7 col0\" >0.7471</td>\n",
       "      <td id=\"T_5ea09_row7_col1\" class=\"data row7 col1\" >0.7802</td>\n",
       "      <td id=\"T_5ea09_row7_col2\" class=\"data row7 col2\" >0.4513</td>\n",
       "      <td id=\"T_5ea09_row7_col3\" class=\"data row7 col3\" >0.7346</td>\n",
       "      <td id=\"T_5ea09_row7_col4\" class=\"data row7 col4\" >0.5591</td>\n",
       "      <td id=\"T_5ea09_row7_col5\" class=\"data row7 col5\" >0.3957</td>\n",
       "      <td id=\"T_5ea09_row7_col6\" class=\"data row7 col6\" >0.4187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5ea09_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_5ea09_row8_col0\" class=\"data row8 col0\" >0.7300</td>\n",
       "      <td id=\"T_5ea09_row8_col1\" class=\"data row8 col1\" >0.7471</td>\n",
       "      <td id=\"T_5ea09_row8_col2\" class=\"data row8 col2\" >0.4437</td>\n",
       "      <td id=\"T_5ea09_row8_col3\" class=\"data row8 col3\" >0.6855</td>\n",
       "      <td id=\"T_5ea09_row8_col4\" class=\"data row8 col4\" >0.5388</td>\n",
       "      <td id=\"T_5ea09_row8_col5\" class=\"data row8 col5\" >0.3600</td>\n",
       "      <td id=\"T_5ea09_row8_col6\" class=\"data row8 col6\" >0.3771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5ea09_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_5ea09_row9_col0\" class=\"data row9 col0\" >0.7381</td>\n",
       "      <td id=\"T_5ea09_row9_col1\" class=\"data row9 col1\" >0.7478</td>\n",
       "      <td id=\"T_5ea09_row9_col2\" class=\"data row9 col2\" >0.4665</td>\n",
       "      <td id=\"T_5ea09_row9_col3\" class=\"data row9 col3\" >0.6962</td>\n",
       "      <td id=\"T_5ea09_row9_col4\" class=\"data row9 col4\" >0.5587</td>\n",
       "      <td id=\"T_5ea09_row9_col5\" class=\"data row9 col5\" >0.3826</td>\n",
       "      <td id=\"T_5ea09_row9_col6\" class=\"data row9 col6\" >0.3981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5ea09_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
       "      <td id=\"T_5ea09_row10_col0\" class=\"data row10 col0\" >0.7380</td>\n",
       "      <td id=\"T_5ea09_row10_col1\" class=\"data row10 col1\" >0.7546</td>\n",
       "      <td id=\"T_5ea09_row10_col2\" class=\"data row10 col2\" >0.4115</td>\n",
       "      <td id=\"T_5ea09_row10_col3\" class=\"data row10 col3\" >0.7377</td>\n",
       "      <td id=\"T_5ea09_row10_col4\" class=\"data row10 col4\" >0.5256</td>\n",
       "      <td id=\"T_5ea09_row10_col5\" class=\"data row10 col5\" >0.3649</td>\n",
       "      <td id=\"T_5ea09_row10_col6\" class=\"data row10 col6\" >0.3957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5ea09_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
       "      <td id=\"T_5ea09_row11_col0\" class=\"data row11 col0\" >0.0185</td>\n",
       "      <td id=\"T_5ea09_row11_col1\" class=\"data row11 col1\" >0.0197</td>\n",
       "      <td id=\"T_5ea09_row11_col2\" class=\"data row11 col2\" >0.0520</td>\n",
       "      <td id=\"T_5ea09_row11_col3\" class=\"data row11 col3\" >0.0530</td>\n",
       "      <td id=\"T_5ea09_row11_col4\" class=\"data row11 col4\" >0.0465</td>\n",
       "      <td id=\"T_5ea09_row11_col5\" class=\"data row11 col5\" >0.0493</td>\n",
       "      <td id=\"T_5ea09_row11_col6\" class=\"data row11 col6\" >0.0482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fbdc313b490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing:   0%|          | 0/7 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n",
      "Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one).\n"
     ]
    }
   ],
   "source": [
    "rf = create_model('rf')\n",
    "rf_tuned = tune_model(rf, optimize = 'AUC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21774bd0-752e-4846-a246-c467122b82e7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "environment": {
   "kernel": "python3",
   "name": "tf2-gpu.2-6.m84",
   "type": "gcloud",
   "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-6:m84"
  },
  "kernelspec": {
   "display_name": "Python 3",
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   "name": "python3"
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
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 "nbformat": 4,
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